Advances in Intelligent Systems and Computing 434

Suresh Chandra SatapathyJyotsna Kumar MandalSiba K. UdgataVikrant Bhateja Editors

Information Systems Design and Intelligent ApplicationsProceedings of Third International Conference INDIA 2016, Volume 2

Advances in Intelligent Systems and Computing

Volume 434

Series editor

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Polande-mail: kacprzyk@ibspan.waw.pl

About this Series

The series “Advances in Intelligent Systems and Computing” contains publications on theory,applications, and design methods of Intelligent Systems and Intelligent Computing. Virtuallyall disciplines such as engineering, natural sciences, computer and information science, ICT,economics, business, e-commerce, environment, healthcare, life science are covered. The listof topics spans all the areas of modern intelligent systems and computing.

The publications within “Advances in Intelligent Systems and Computing” are primarilytextbooks and proceedings of important conferences, symposia and congresses. They coversignificant recent developments in the field, both of a foundational and applicable character.An important characteristic feature of the series is the short publication time and world-widedistribution. This permits a rapid and broad dissemination of research results.

Advisory Board

Chairman

Nikhil R. Pal, Indian Statistical Institute, Kolkata, Indiae-mail: nikhil@isical.ac.in

Members

Rafael Bello, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cubae-mail: rbellop@uclv.edu.cu

Emilio S. Corchado, University of Salamanca, Salamanca, Spaine-mail: escorchado@usal.es

Hani Hagras, University of Essex, Colchester, UKe-mail: hani@essex.ac.uk

László T. Kóczy, Széchenyi István University, Győr, Hungarye-mail: koczy@sze.hu

Vladik Kreinovich, University of Texas at El Paso, El Paso, USAe-mail: vladik@utep.edu

Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwane-mail: ctlin@mail.nctu.edu.tw

Jie Lu, University of Technology, Sydney, Australiae-mail: Jie.Lu@uts.edu.au

Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexicoe-mail: epmelin@hafsamx.org

Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazile-mail: nadia@eng.uerj.br

Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Polande-mail: Ngoc-Thanh.Nguyen@pwr.edu.pl

Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Konge-mail: jwang@mae.cuhk.edu.hk

More information about this series at http://www.springer.com/series/11156

Suresh Chandra SatapathyJyotsna Kumar Mandal • Siba K. UdgataVikrant BhatejaEditors

Information Systems Designand Intelligent ApplicationsProceedings of Third InternationalConference INDIA 2016, Volume 2

123

EditorsSuresh Chandra SatapathyDepartment of Computer Scienceand Engineering

Anil Neerukonda Institute of Technologyand Sciences

VisakhapatnamIndia

Jyotsna Kumar MandalKalyani UniversityNadia, West BengalIndia

Siba K. UdgataUniversity of HyderabadHyderabadIndia

Vikrant BhatejaDepartment of Electronics andCommunication Engineering

Shri Ramswaroop Memorial Groupof Professional Colleges

Lucknow, Uttar PradeshIndia

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-81-322-2750-2 ISBN 978-81-322-2752-6 (eBook)DOI 10.1007/978-81-322-2752-6

Library of Congress Control Number: 2015960416

© Springer India 2016, corrected publication 2018This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made.

Printed on acid-free paper

This Springer imprint is published by SpringerNatureThe registered company is Springer (India) Pvt. Ltd.

Preface

The papers in this volume were presented at the INDIA 2016: Third InternationalConference on Information System Design and Intelligent Applications. Thisconference was organized by the Department of CSE of Anil Neerukonda Instituteof Technology and Sciences (ANITS) and ANITS CSI Student Branch with tech-nical support of CSI, Division-V (Education and Research) during 8–9 January2016. The conference was hosted in the ANITS campus. The objective of thisinternational conference was to provide opportunities for researchers, academicians,industry personas and students to interact and exchange ideas, experience andexpertise in the current trends and strategies for Information and IntelligentTechniques. Research submissions in various advanced technology areas werereceived and after a rigorous peer-review process with the help of programmecommittee members and external reviewers, 215 papers in three separate volumes(Volume I: 75, Volume II: 75, Volume III: 65) were accepted with an acceptanceratio of 0.38. The conference featured seven special sessions in various cutting edgetechnologies, which were conducted by eminent professors. Many distinguishedpersonalities like Dr. Ashok Deshpande, Founding Chair: Berkeley Initiative in SoftComputing (BISC)—UC Berkeley CA; Guest Faculty, University of CaliforniaBerkeley; Visiting Professor, University of New South Wales Canberra and IndianInstitute of Technology Bombay, Mumbai, India, Dr. Parag Kulkarni, Pune;Dr. Aynur Ünal, Strategic Adviser and Visiting Full Professor, Department ofMechanical Engineering, IIT Guwahati; Dr. Goutam Sanyal, NIT, Durgapur;Dr. Naeem Hannoon, Universiti Teknologi MARA, Shah Alam, Malaysia;Dr. Rajib Mall, Indian Institute of Technology Kharagpur, India; Dr. B. Majhi,NIT-Rourkela; Dr. Vipin Tyagi, Jaypee University of Engineering and Technology,Guna; Prof. Bipin V. Mehta, President CSI; Dr. Durgesh Kumar Mishra, Chairman,Div-IV, CSI; Dr. Manas Kumar Sanyal, University of Kalyani; Prof. Amit Joshi,Sabar Institute, Gujarat; Dr. J.V.R. Murthy, JNTU, Kakinada; Dr. P.V.G.D. PrasadReddy, CoE, Andhra University; Dr. K. Srujan Raju, CMR Technical Campus,Hyderabad; Dr. Swagatam Das, ISI Kolkata; Dr. B.K. Panigrahi, IIT Delhi;Dr. V. Suma, Dayananda Sagar Institute, Bangalore; Dr. P.S. Avadhani,

v

Vice-Principal, CoE(A), Andhra University and Chairman of CSI, Vizag Chapter,and many more graced the occasion as distinguished speaker, session chairs,panelist for panel discussions, etc., during the conference days.

Our sincere thanks to Dr. Neerukonda B.R. Prasad, Chairman, Shri V.Thapovardhan, Secretary and Correspondent, Dr. R. Govardhan Rao, Director(Admin) and Prof. V.S.R.K. Prasad, Principal of ANITS for their excellent supportand encouragement to organize this conference of such magnitude.

Thanks are due to all special session chairs, track managers and distinguishedreviewers for their timely technical support. Our entire organizing committee, staffof CSE department and student volunteers deserve a big pat for their tireless effortsto make the event a grand success. Special thanks to our Programme Chairs forcarrying out an immaculate job. We place our special thanks here to our publicationchairs, who did a great job to make the conference widely visible.

Lastly, our heartfelt thanks to all authors without whom the conference wouldnever have happened. Their technical contributions made our proceedings rich andpraiseworthy. We hope that readers will find the chapters useful and interesting.

Our sincere thanks to all sponsors, press, print and electronic media for theirexcellent coverage of the conference.

November 2015 Suresh Chandra SatapathyJyotsna Kumar Mandal

Siba K. UdgataVikrant Bhateja

vi Preface

Organizing Committee

Chief Patrons

Dr. Neerukonda B.R. Prasad, Chairman, ANITSShri V. Thapovardhan, Secretary and Correspondent, ANITS, Visakhapatnam

Patrons

Prof. V.S.R.K. Prasad, Principal, ANITS, VisakhapatnamProf. R. Govardhan Rao, Director-Admin, ANITS, Visakhapatnam

Honorary Chairs

Dr. Bipin V. Mehta, President CSI, IndiaDr. Anirban Basu, Vice-President, CSI, India

Advisory Committee

Prof. P.S. Avadhani, Chairman, CSI Vizag Chapter, Vice Principal, AU College ofEngineeringShri D.N. Rao, Vice Chairman and Chairman (Elect), CSI Vizag Chapter, Director(Operations), RINL, Vizag Steel PlantShri Y. Madhusudana Rao, Secretary, CSI Vizag Chapter, AGM (IT), Vizag SteelPlantShri Y. Satyanarayana, Treasurer, CSI Vizag Chapter, AGM (IT), Vizag Steel Plant

Organizing Chair

Dr. Suresh Chandra Satapathy, ANITS, Visakhapatnam

Organizing Members

All faculty and staff of Department of CSE, ANITSStudents Volunteers of ANITS CSI Student Branch

vii

Program Chair

Dr. Manas Kumar Sanayal, University of Kalyani, West BengalProf. Pritee Parwekar, ANITS

Publication Chair

Prof. Vikrant Bhateja, SRMGPC, Lucknow

Publication Co-chair

Mr. Amit Joshi, CSI Udaipur Chapter

Publicity Committee

Chair: Dr. K. Srujan Raju, CMR Technical Campus, HyderabadCo-chair: Dr. Venu Madhav Kuthadi,Department of Applied Information SystemsFaculty of ManagementUniversity of JohannesburgAuckland Park, Johannesburg, RSA

Special Session Chairs

Dr. Mahesh Chandra, BIT Mesra, India, Dr. Asutosh Kar, BITS, Hyderabad:“Modern Adaptive Filtering Algorithms and Applications for Biomedical SignalProcessing Designs”Dr. Vipin Tyagi, JIIT, Guna: “Cyber Security and Digital Forensics”Dr. Anuja Arora, Dr. Parmeet, Dr. Shikha Mehta, JIIT, Noida-62: “Recent Trendsin Data Intensive Computing and Applications”Dr. Suma, Dayananda Sagar Institute, Bangalore: “Software Engineering and itsApplications”Hari Mohan Pandey, Ankit Chaudhary: “Patricia Ryser-Welch, Jagdish Raheja”,“Hybrid Intelligence and Applications”Hardeep Singh, Punjab: “ICT, IT Security & Prospective in Science, Engineering &Management”Dr. Divakar Yadav, Dr. Vimal Kumar, JIIT, Noida-62: “Recent Trends inInformation Retrieval”

Track Managers

Track #1: Image Processing, Machine Learning and Pattern Recognition—Dr. StevenL. FernandezTrack #2: Data Engineering—Dr. Sireesha RoddaTrack #3: Software Engineering—Dr. Kavita ChoudharyTrack #4: Intelligent Signal Processing and Soft Computing—Dr. Sayan Chakraborty

viii Organizing Committee

Technical Review Committee

Akhil Jose Aei, Vimaljyothi Engineering College (VJEC), Kannur, Kerala, India.Alvaro Suárez Sarmiento, University of Las Palmas de Gran Canaria.Aarti Singh, MMICTBM, M.M. University, Mullana, India.Agnieszka Boltuc, University of Bialystok, Poland.Anandi Giri, YMT college of Management, Navi Mumbai, India.Anil Gulabrao Khairnar, North Maharashtra University, Jalgaon, India.Anita Kumari, Lovely Professional University, Jalandhar, PunjabAnita M. Thengade, MIT COE Pune, India.Arvind Pandey, MMMUT, Gorakhpur (U.P.), India.Banani Saha, University of Calcutta, India.Bharathi Malakreddy, JNTU Hyderabad, India.Bineet Kumar Joshi, ICFAI University, Dehradun, India.Chhayarani Ram Kinkar, ICFAI, Hyderabad, India.Chirag Arora, KIET, Ghaziabad (U.P.), India.C. Lakshmi Devasena, IFHE University, Hyderabad, India.Charan S.G., Alcatel-Lucent India Limited, Bangalore, IndiaDac-Nhuong Le, VNU University, Hanoi, Vietnam.Emmanuel C. Manasseh, Tanzania Communications Regulatory Authority (TCRA)Fernando Bobillo Ortega, University of Zaragoza, Spain.Frede Blaabjerg, Department of Energy Technology, Aalborg University, Denmark.Foued Melakessou, University of Luxembourg, LuxembourgG.S. Chandra Prasad, Matrusri Engineering College, Saidabad, HyderabadGustavo Fernandez, Austrian Institute of Technology, Vienna, AustriaIgor N. Belyh, St. Petersburg State Polytechnical UniversityJignesh G. Bhatt, Dharmsinh Desai University, Gujarat, India.Jyoti Yadav, Netaji Subhas Institute of Technology, New Delhi, India.K. Kalimathu, SRM University, India.Kamlesh Verma, IRDE, DRDO, Dehradun, India.Karim Hashim Kraidi, The University of Al-Mustansiriya, Baghdad, IraqKrishnendu Guha, University of Calcutta, India

ix

Lalitha RVS, Sri Sai Aditya Institute of Science and Technology, India.M. Fiorini, PolandMahdin Mahboob, Stony Brook University.Mahmood Ali Mirza, DMS SVH College of Engineering (A.P.), IndiaManimegalai C.T., SRM University, India.Mp Vasudha, Jain University Bangalore, India.Nikhil Bhargava, CSI ADM, Ericsson, India.Nilanjan Dey, BCET, Durgapur, India.Pritee Parweker, ANITS, VisakhapatnamSireesha Rodda, GITAM, VisakhapatnamParama Bagchi, IndiaCh Seshadri Rao, ANITS, VisakhapatnamPramod Kumar Jha, Centre for Advanced Systems (CAS), India.Pradeep Kumar Singh, Amity University, Noida, U.P. India.Ramesh Sunder Nayak, Canara Engineering College, India.R.K. Chauhan, MMMUT, Gorakhpur (U.P.), IndiaRajiv Srivastava, Scholar tech Education, India.Ranjan Tripathi, SRMGPC, Lucknow (U.P.), India.S. Brinda, St. Joseph’s Degree and PG College, Hyderabad, India.Sabitha G., SRM University, India.Suesh Limkar, AISSMS IOIT, PuneY.V.S.M. Murthy, NIT, SurathkalB.N. Biswal, BEC, BhubaneswarMihir Mohanty, SOA, BhubaneswarS. Sethi, IGIT, SarangSangeetha M., SRM University, India.Satyasai Jagannath Nanda, Malaviya National Institute of Technology Jaipur, India.Saurabh Kumar Pandey, National Institute of Technology, Hamirpur (H.P.), India.Sergio Valcarcel, Technical University of Madrid, Spain.Shanthi Makka, JRE School of Engineering, Gr. Noida (U.P.), India.Shilpa Bahl, KIIT, Gurgaon, India.Sourav De, University Institute of Technology, BU, India.Sourav Samanta, University Institute of Technology, BU, India.Suvojit Acharjee, NIT, Agartala, India.Sumit Soman, C-DAC, Noida (U.P.), India.Usha Batra, ITM University, Gurgaon, India.Vimal Mishra, MMMUT, Gorakhpur (U.P.), India.Wan Khairunizam Wan Ahmad, AICOS Research Lab, School of Mechatronic,UniMAP.Yadlapati Srinivasa Kishore Babu, JNTUK, Vizianagaram, India.

x Technical Review Committee

Contents

A Short Run Length Descriptor for Image Retrieval. . . . . . . . . . . . . . . 1Nishant Shrivastava and Vipin Tyagi

A New Curvelet Based Blind Semi-fragile Watermarking Schemefor Authentication and Tamper Detection of Digital Images . . . . . . . . . 11S. Nirmala and K.R. Chetan

Indexing Video by the Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Mohammed Amin Belarbi, Saïd Mahmoudi and Ghalem Belalem

EIDPS: An Efficient Approach to Protect the Network and IntrusionPrevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Rajalakshmi Selvaraj, Venu Madhav Kuthadi and Tshilidzi Marwala

Slot Utilization and Performance Improvement in Hadoop Cluster . . . . 49K. Radha and B. Thirumala Rao

An Efficient Educational Data Mining Approach to SupportE-learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Padmaja Appalla, Venu Madhav Kuthadi and Tshilidzi Marwala

A Re-ranking Approach Personalized Web Search Resultsby Using Privacy Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Vijayalakshmi Kakulapati and Sunitha Devi Bigul

An Efficient Virtualization Server Infrastructure for e-Schoolsof India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89Gantasala Sai Hemanth and Sk. Noor Mahammad

A Novel Approach for Horizontal Privacy Preserving Data Mining . . . . 101Hanumantha Rao Jalla and P.N. Girija

Randomized Cryptosystem Based on Linear Transformation . . . . . . . . 113K. Adi Narayana Reddy, B. Vishnuvardhanand G. Shyama Chandra Prasad

xi

Literature Survey to Improve Image Retrieval Efficiencyby Visual Attention Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121T. Venkata Ramana, K. Venugopal Rao and G. Shyama Chandra Prasad

Substrate Integrated Waveguide Based 4-Port Crossoverfor Beam-Forming Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129P. Rahul Lal, Prasanth M. Warrier and Sreedevi K. Menon

Computational Model of Pixel Wise Temperature Predictionfor Liver Tumor by High Intensity Focused Ultrasound Ablations . . . . 137P. Revathy and V. Sadasivam

Implementation of Server Load Balancing in Software DefinedNetworking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Sukhveer Kaur and Japinder Singh

Building Stateful Firewall Over Software Defined Networking. . . . . . . . 159Karamjeet Kaur and Japinder Singh

A Proposed Framework to Adopt Mobile App in ‘e-District’ Projectsto Move One Step Ahead for Successful Implementation. . . . . . . . . . . . 169Manas Kumar Sanyal, Sudhangsu Das and Sajal Bhadra

A Semantic Framework to Standardize Cloud Adoption Process . . . . . . 179Kumar Narander and Saxena Swati

Analysis of Efficiency of Classification and Prediction Algorithms(kNN) for Breast Cancer Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187G.D. Rashmi, A. Lekha and Neelam Bawane

A Novel Genetic Algorithm and Particle Swarm Optimizationfor Data Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Malini Devi Gandamalla, Seetha Maddala and K.V.N. Sunitha

Design Issues of Big Data Parallelisms . . . . . . . . . . . . . . . . . . . . . . . . . 209Koushik Mondal

Revised ECLAT Algorithm for Frequent Itemset Mining . . . . . . . . . . . 219Bharati Suvalka, Sarika Khandelwal and Chintal Patel

User-Interface Design Framework for E-Learning Through MobileDevices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227Ankita Podder, Tanushree Bhadra and Rajeev Chatterjee

Application of Three Different Artificial Neural NetworkArchitectures for Voice Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Bageshree Sathe-Pathak, Shalaka Patil and Ashish Panat

Foreground Background Segmentation for Video ProcessingThrough Dynamic Hand Gestures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247C. Swapna and Shabnam S. Shaikh

xii Contents

Load Encroachment Detection Algorithm for Digital Signal ProcessorBased Numerical Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Shanker Warathe and R.N. Patel

K-Nearest Neighbor and Boundary Cutting Algorithm for IntrusionDetection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269Punam Mulak, D.P. Gaikwad and N.R. Talhar

Analysis of Electromagnetic Radiation from Cellular NetworksConsidering Geographical Distance and Height of the Antenna . . . . . . . 279S. Venkatesulu, S. Varadarajan, M. Sadasiva, A. Chandrababu and L. HaraHara Brahma

Revitalizing VANET Communication Using Bluetooth Devices . . . . . . . 289G. Jaya Suma and R.V.S. Lalitha

Virtual Manipulation: An On-Screen Interactive Visual Designfor Science Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299Sanju Saha and Santoshi Halder

Bundle Block Detection Using Genetic Neural Network. . . . . . . . . . . . . 309Padmavathi Kora and K. Sri Rama Krishna

Tools for Data Visualization in Business Intelligence: Case StudyUsing the Tool Qlikview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Ayushi Shukla and Saru Dhir

Application of Support Vector Machines for Fast and AccurateContingency Ranking in Large Power System . . . . . . . . . . . . . . . . . . . 327Soni Bhanu Pratap, Saxena Akash and Gupta Vikas

A Novel Circular Monopole Fractal Antenna for Bluetooth and UWBApplications with Subsequent Increase in Gain Using FrequencySelective Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Divyanshu Upadhyay, Indranil Acharya and Ravi Prakash Dwivedi

Application of Clustering for Improving Search Resultof a Website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349Shashi Mehrotra and Shruti Kohli

Performance Evaluation of Basic Selfish Node Detection Strategyon Delay Tolerant Networking Routing Protocols . . . . . . . . . . . . . . . . . 357Upendra B. Malekar and Shailesh P. Hulke

Dynamic Texture Recognition: A Review . . . . . . . . . . . . . . . . . . . . . . . 365Deepshika Tiwari and Vipin Tyagi

Contents xiii

Performance Analysis of Fully Depleted Ultra Thin-Body (FD UTB)SOI MOSFET Based CMOS Inverter Circuit for Low Power DigitalApplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Vimal Kumar Mishra and R.K. Chauhan

Mathematical Vector Quantity Modulated Three Phase Four LegInverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383Bhaskar Bhattacharya and Ajoy Kumar Chakraborty

21st C New Technologies a Pathway to Inclusive and SustainableGrowth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395Pradnya Chitrao

Dual Image Based Reversible Data Hiding Scheme Using Three PixelValue Difference Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Giri Debasis, Jana Biswapati and Mondal Shyamal Kumar

Design of Adaptive Filter Using Vedic Multiplier for Low Power . . . . . 413Ch. Pratyusha Chowdari and J. Beatrice Seventline

Issues and Approaches to Design of a Range Image Face Database . . . . 425Suranjan Ganguly, Debotosh Bhattacharjee and Mita Nasipuri

A Secure Homomorphic Routing Technique in Wireless MeshNetwork (HRT for WMN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437Geetanjali Rathee and Hemraj Saini

Fuzzy Based Fault Location Estimation During UnearthedOpen Conductor Faults in Double Circuit Transmission Line . . . . . . . . 445Aleena Swetapadma and Anamika Yadav

Optimization in Round Robin Process Scheduling Algorithm . . . . . . . . 457Anurag Upadhyay and Hitesh Hasija

Frequency Dependent Lumped Model of Twin Band MIMOAntenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469Vilas V. Mapare and G.G. Sarate

A Novel Framework for Integrating Data Mining Techniquesto Software Development Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485B.V. Ajay Prakash, D.V. Ashoka and V.N. Manjunath Aradhya

Survey of Improved k-means Clustering Algorithms: Improvements,Shortcomings and Scope for Further Enhancement and Scalability . . . . 495Anand Khandare and A.S. Alvi

Attack Identification Framework for IoT Devices . . . . . . . . . . . . . . . . . 505Jagan Mohan Reddy Danda and Chittaranjan Hota

xiv Contents

Compact Coalescence Clustering Algorithm (C3A)—A GISAnchored Approach of Clustering Discrete Points . . . . . . . . . . . . . . . . 515Anirban Chakraborty, Jyotsna Kumar Mandal, Pallavi Royand Pratyusha Bhattacharya

Miniaturized UWB BPF with a Notch Band at 5.8 GHzUsing Cascaded Structure of Highpass and Lowpass Filter . . . . . . . . . . 525Arvind Kumar Pandey, Yatindra Gaurav and R.K. Chauhan

Dual-Band Microstrip Fed Monopole Patch Antenna for WiMAXand WLAN Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533Chandan and B.S. Rai

Script Based Trilingual Handwritten Word Level Multiple SkewEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541M. Ravikumar, D.S. Guru, S. Manjunath and V.N. Manjunath Aradhya

Deep Convolutional Neural Network Classifier for HandwrittenDevanagari Character Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . 551Pratibha Singh, Ajay Verma and Narendra S. Chaudhari

A Clustering-Based Generic Interaction Protocol for MultiagentSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563Dimple Juneja, Rashmi Singh, Aarti Singh and Saurabh Mukherjee

An Improved Content Based Medical Image Retrieval SystemUsing Integrated Steerable Texture Components and User InteractiveFeedback Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573B. Jyothi, Y. Madhavee Latha and P.G. Krishna Mohan

Improvement of Stability by Optimal Location with TuningSTATCOM Using Particle Swarm Optimization Algorithm. . . . . . . . . . 583P.K. Dhal

Identifying Crop Specific Named Entities from Agriculture DomainUsing Semantic Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595Ashish Kumar, Payal Biswas and Aditi Sharan

MED-HYREC: A Recommendation System for Medical Domain. . . . . . 605Venkata A. Paruchuri

Statistical and Linguistic Knowledge Based Speech RecognitionSystem: Language Acquisition Device for Machines . . . . . . . . . . . . . . . 613Challa Sushmita, Challa Nagasai Vijayshriand Krishnaveer Abhishek Challa

Energy Stability in Cloud for Web Page Ranking. . . . . . . . . . . . . . . . . 621Sutirtha Kumar Guha, Anirban Kundu and Rana Dattagupta

Web Based System Design Using Session Relevancy . . . . . . . . . . . . . . . 631Sutirtha Kumar Guha, Anirban Kundu and Rana Dattagupta

Contents xv

Design of IOT based Architecture Using Real Time Data . . . . . . . . . . . 641Nivedita Ray De Sarkar, Anirban Kundu, Anupam Beraand Mou De

Performance Evaluation of Classifier Combination Techniquesfor the Handwritten Devanagari Character Recognition . . . . . . . . . . . . 651Pratibha Singh, Ajay Verma and Narendra S. Chaudhari

An Improved Kuan Algorithm for Despeckling of SAR Images . . . . . . . 663Aditi Sharma, Vikrant Bhateja and Abhishek Tripathi

GSM Based Automated Detection Model for Improvised ExplosiveDevices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673Rajat Sharma, Vikrant Bhateja and S.C. Satapathy

Review on Video Watermarking Techniques in Spatialand Transform Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683Garima Gupta, V.K. Gupta and Mahesh Chandra

Musical Noise Reduction Capability of Various SpeechEnhancement Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693Prateek Saxena, V.K. Gupta and Mahesh Chandra

Model with Cause and Effect for MANET (M-CEM) . . . . . . . . . . . . . . 703Sharma Pankaj, Kohli Shruti and Sinha K. Ashok

Parameters Quantification of Genetic Algorithm. . . . . . . . . . . . . . . . . . 711Hari Mohan Pandey

Emotion Recognition: A Step Ahead of Traditional Approaches . . . . . . 721Surbhi Agarwal, Madhulika Bhatia and Madhurima Hooda

Evaluation of Genetic Algorithm’s Selection Methods . . . . . . . . . . . . . . 731Hari Mohan Pandey, Anupriya Shukla, Ankit Chaudharyand Deepti Mehrotra

An Amalgamated Strategy for Iris Recognition Employing NeuralNetwork and Hamming Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739Madhulika Pandey

A Comparative Investigation of Sample Versus Normal Mapfor Effective BigData Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749Shyavappa Yalawar, V. Suma and Jawahar Rao

Big Data Management System for Personal PrivacyUsing SW and SDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757Kashinath Sarjapur, V. Suma, Sharon Christa and Jawahar Rao

xvi Contents

Erratum to: An Amalgamated Strategy for Iris RecognitionEmploying Neural Network and Hamming Distance . . . . . . . . . . . . . . . E1Madhulika Pandey and Madhulika Bhatia

Erratum to: Emotion Recognition: A Step Aheadof Traditional Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E3Surbhi Agarwal, Madhulika Bhatia and Madhurima Hooda

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765

Contents xvii

About the Editors

Dr. Suresh Chandra Satapathy is currently working as Professor and Head,Department of Computer Science and Engineering, Anil Neerukonda Institute ofTechnology and Sciences (ANITS), Visakhapatnam, Andhra Pradesh, India.He obtained his Ph.D. in Computer Science Engineering from JNTUH, Hyderabadand his Master’s degree in Computer Science and Engineering from NationalInstitute of Technology (NIT), Rourkela, Odisha. He has more than 27 years ofteaching and research experience. His research interests include machine learning,data mining, swarm intelligence studies and their applications to engineering.He has more than 98 publications to his credit in various reputed internationaljournals and conference proceedings. He has edited many volumes fromSpringer AISC and LNCS in the past and he is also the editorial board member of afew international journals. He is a senior member of IEEE and Life Member ofComputer society of India. Currently, he is the National Chairman of Division-V(Education and Research) of Computer Society of India.

Dr. Jyotsna Kumar Mandal has an M.Sc. in Physics from Jadavpur University in1986, M.Tech. in Computer Science from University of Calcutta. He was awardedthe Ph.D. in Computer Science & Engineering by Jadavpur University in 2000.Presently, he is working as Professor of Computer Science & Engineering andformer Dean, Faculty of Engineering, Technology and Management, KalyaniUniversity, Kalyani, Nadia, West Bengal for two consecutive terms. He started hiscareer as lecturer at NERIST, Arunachal Pradesh in September, 1988. He hasteaching and research experience of 28 years. His areas of research include codingtheory, data and network security, remote sensing and GIS-based applications, datacompression, error correction, visual cryptography, steganography, security inMANET, wireless networks and unify computing. He has produced 11 Ph.D.degrees of which three have been submitted (2015) and eight are ongoing. He hassupervised 3 M.Phil. and 30 M.Tech. theses. He is life member of ComputerSociety of India since 1992, CRSI since 2009, ACM since 2012, IEEE since 2013and Fellow member of IETE since 2012, Executive member of CSI KolkataChapter. He has delivered invited lectures and acted as programme chair of many

xix

international conferences and also edited nine volumes of proceedings fromSpringer AISC series, CSI 2012 from McGraw-Hill, CIMTA 2013 from ProcediaTechnology, Elsevier. He is reviewer of various international journals and con-ferences. He has over 355 articles and 5 books published to his credit.

Dr. Siba K. Udgata is a Professor of School of Computer and InformationSciences, University of Hyderabad, India. He is presently heading Centre forModelling, Simulation and Design (CMSD), a high-performance computing facilityat University of Hyderabad. He obtained his Master’s followed by Ph.D. inComputer Science (mobile computing and wireless communication). His mainresearch interests include wireless communication, mobile computing, wirelesssensor networks and intelligent algorithms. He was a United Nations Fellow andworked in the United Nations University/International Institute for SoftwareTechnology (UNU/IIST), Macau, as research fellow in the year 2001. Dr. Udgata isworking as principal investigator in many Government of India funded researchprojects, mainly for development of wireless sensor network applications andapplication of swarm intelligence techniques. He has published extensively inrefereed international journals and conferences in India and abroad. He was also onthe editorial board of many Springer LNCS/LNAI and Springer AISC Proceedings.

Prof. Vikrant Bhateja is Associate Professor, Department of Electronics andCommunication Engineering, Shri Ramswaroop Memorial Group of ProfessionalColleges (SRMGPC), Lucknow, and also the Head (Academics & Quality Control)in the same college. His areas of research include digital image and video pro-cessing, computer vision, medical imaging, machine learning, pattern analysis andrecognition, neural networks, soft computing and bio-inspired computing tech-niques. He has more than 90 quality publications in various international journalsand conference proceedings. Professor Vikrant has been on TPC and chaired var-ious sessions from the above domain in international conferences of IEEE andSpringer. He has been the track chair and served in the core-technical/editorialteams for international conferences: FICTA 2014, CSI 2014 and INDIA 2015 underSpringer-ASIC Series and INDIACom-2015, ICACCI-2015 under IEEE. He isassociate editor in International Journal of Convergence Computing (IJConvC) andalso serves on the editorial board of International Journal of Image Mining (IJIM)under Inderscience Publishers. At present, he is guest editor for two special issuesfloated in International Journal of Rough Sets and Data Analysis (IJRSDA) andInternational Journal of System Dynamics Applications (IJSDA) under IGI Globalpublications.

xx About the Editors

A Short Run Length Descriptor for ImageRetrieval

Nishant Shrivastava and Vipin Tyagi

Abstract In this paper an image retrieval technique based on a novel Short RunLength Descriptor (SRLD) is proposed. SRLD can effectively represent image localand global information. It can be viewed as an integrated representation of bothcolor and texture properties. HSV color space is quantized to 72 bins and SRLD iscomputed using short run lengths of size two and three for each color in differentorientations. Short run lengths at all orientations are combined to get Short RunLength Histogram (SRLH) feature. SRLH can thoroughly describe the spatialcorrelation between color and texture and have the advantages of both statisticaland structural approaches of texture representation. The experimental results clearlydemonstrate the effectiveness of the proposed descriptor in image retrievalapplications.

Keywords Run length histogram � Color descriptor � Texture descriptor � Textondetection � Color quantization

1 Introduction

In the recent years, growth of multimedia information from various sources hasincreased many folds. This has created the demand of an accurate Content BasedImage Retrieval Systems (CBIR). Success of a general purpose CBIR largelydepends upon the effectiveness of descriptors used to represent images. Low levelfeatures like color, texture and shape have been used for describing content of theimages. The shape descriptors depends on the accuracy of the image segmentation

N. Shrivastava (&) � V. TyagiDepartment of Computer Science and Engineering, Jaypee University of Engineeringand Technology, Raghogarh, 473226 Guna, MP, Indiae-mail: nishantuit@gmail.com

V. Tyagie-mail: dr.vipin.tyagi@gmail.com

© Springer India 2016S.C. Satapathy et al. (eds.), Information Systems Design and IntelligentApplications, Advances in Intelligent Systems and Computing 434,DOI 10.1007/978-81-322-2752-6_1

1

technique employed [1]. The accurate segmentation of images is still an openproblem. Therefore a large number of CBIR techniques rely heavily on color andtexture information of the image. Color and texture can provide the robust featureset for providing most discriminating information for natural images [2–4].

Color and texture integration using local binary patters have shown to obtain agood retrieval accuracy [5, 6]. Also many researchers have shown to integrate boththe color and texture in a single descriptor using Textons [7–10]. A texton can bedefined as the structural element used to capture the run lengths in an image at aparticular orientation. However, the structure of textons is rigid. Moving Textonsover the image may ignore some finer texture details which may be important forimage discrimination. Each texton is moved separately for capturing textureinformation in a particular orientation. Individual texton maps are then combined tomake a integrated descriptor for representing color and texture of the image. Thisconsumes a lot of time.

To improve the texton representation, in this paper, a novel texture descriptorSRLD and its histogram SRLH is proposed. The images are quantized into 72 maincolors in HSV color space. Short run length of each color of size 2 and 3 areextracted from the image. The size is chosen to overcome the limitation of textonbased approaches which uses matrix of size 2 × 2 or 3 × 3 to extract texturefeatures. Run lengths at each orientation are combined to make the final SRLH. Theproposed SRLH can describe the correlation between color and texture in a detailedmanner and have the advantages of both statistical and structural approaches ofextracting texture. SRLH can be seen as an integrated representation of informationgained from all type of textons together.

The rest of the paper is organized as follows: Sect. 2 describes the related work.Proposed SRLD and SRLH descriptors are discussed in Sect. 3. Experimentalresults are demonstrated in Sect. 4 and conclusions are summed up in Sect. 5.

2 Related Work

Various descriptors have been proposed for integrating color and texture of images[7–9, 11–13]. Micro-Structure Descriptors (MSD) [8] utilize underlying colors inmicrostructure with similar edge orientation to represent color, texture and orien-tation information of images. MSD uses 3 × 3 windows to detect microstructure in aquantized color image. Pixels having value similar to centre pixel in window of size3 × 3 are only retained to define microstructures. MSD does not provide detailedcorrelation of color and texture as many textural patterns are left undetected as onlycentre pixel is considered.

Structure Element Descriptor (SED) [9] based scheme uses 2 × 2 matrix asshown in Fig. 1 to extract texture information at different orientation. The image isfirst quantized to 72 bins in HSV color space. Each of the five structuring elementsare moved over the image for detecting textons of each of the 72 colors. A SED isdetected when pixels having same value occur in colored part of template. After

2 N. Shrivastava and V. Tyagi

moving each of the 5 SED, the total count of SED of each type for each color formthe SEH, with each bin representing the total number of SED found of a particulartype for each color, is used to describe the image. SED is not capable of extractingall texture information as run lengths of odd size are not detected properly; alsoSED are redundant and overlap over each other as fifth SED is detected when allfour are detected and vice versa. Moreover, the process takes a lot of time movingSED over the image.

Texton Co-occurrence Matrix (TCM) [10] based technique also uses textons toextract the texture. Textons are moved over the quantized image from left-to-rightand top-to-bottom to detect textons with one pixel as the step-length. If the pixelvalues that fall in the texton template are the same, those pixels will form a texton,and their values are kept as the original values. Otherwise they will be set to zero.Each texton template can lead to a texton image, and five texton templates will leadto five texton images. Finally, all texton images are combined to get a single image.

Multi-Texton histogram (MTH) [7] based image retrieval integrates the advan-tage of co-occurrence matrix and histogram by representing the attribute ofco-occurrence matrix by histogram. MTH uses first four texton out of 5 SED toextract texture from the image. Like SED, MTH also is not able to represent the fullcontent of images. In [3], an adaptive color feature extraction scheme using theBinary Quaternion-Moment Preserving (BQMP) threshold technique is used todescribe the distribution of image.

3 Proposed Method

3.1 Color Quantization in HSV Color Space

Color is the most commonly used feature in the CBIR since it is not affected byrotation, scaling and other transformation on the image. In this paper, we haveselected HSV (Hue, Saturation, Vaue) color space since it is more perceptuallyuniform than other color spaces [14].

To obtain quantized image having 72 colors the images are converted from RGBto HSV color space and a non-uniform quantization technique [15] is used. Onedimensional color feature vector P, is constructed using: P = 9H + 3S + V. Each

Fig. 1 Five texton type defined in SED

A Short Run Length Descriptor for Image Retrieval 3

image is quantized to 72 main colors and SRLD is computed to finally get theSRLH feature of the image. In this paper quantized HSV color space is used toextract both color and texture feature simultaneously.

3.2 Short Run Length Descriptor (SRLD)

The color, texture and shape features are extensively used in representation ofimages in content based image retrieval system. After quantization of image to 72colors in HSV space, the texture information can be extracted using statistical andstructural methods. SED has a limitation that only one type of SED can be used at atime, therefore it cannot describe all the repetitive structure in the image. Figure 4ashows an example portion of image having run length of 3 wrongly represented by2 × 2 SED as of length 2. This confirms that the run lengths of odd size cannot berepresented by SED. Figure 4b shows that the pair of 1 left undetected by movingSED of 2 × 2 with step length of 2 over the image. From Fig. 2, it is obvious thatSED based methods can only represent the local characteristic of image and lacks indetail analysis of texture from the whole image. To integrate the color and textureinformation in a single descriptor including higher details of spatial correlation, wehave proposed a more effective short run length descriptor (SRLD).

Capturing texture information using structuring elements is not flexible and mayresult in loss of some important discriminating texture patterns. The SRLD uses runlengths of size at most 3 to describe different texture structures hence is able todescribe all repetitive texture patterns in the image. The size of run lengths is keptlimited to 2 and 3 as the combination of 2 and 3 can describe any odd and evennumbers. This is analogous to the texton based techniques using matrix of size 2 × 2or 3 × 3 to extract texture. To capture orientation information the run lengths areextracted at 0°, 45°, 90° and 135° for each quantization level in the HSV colorspace. The process of extracting SRLD can be described in a simple 3-step strategyas follows:

1. Starting from (0, 0), scan each row of pixel from top to bottom. To avoid theextraction of wrong run length, counting of pixels terminates at the end of eachrow and start at the beginning of each row.

Fig. 2 An example showing a run length of 3 described by SED as of length 2 b undetected runlength of pair of 1

4 N. Shrivastava and V. Tyagi

2. Compute run lengths with size of at most 3 pixels excluding those of length 1. Ifthe run length size is greater than 2 and 3 then break it into multiple smaller runlength of size 2 and 3.

3. Count the number of run lengths of size 2 and 3 for each color for making finalrun length.

The above steps are used to extract SRLD at orientation of 0°. For other ori-entations the image is scanned in a column to column and diagonal to diagonalbasis. The outcome of this process is a total of 4 run lengths one for each orien-tation. It can be easily observed that the run length representation is similar totexton based methods with more detail texture analysis.

3.3 Short Run Length Histogram

The run length computed above contains 2 entries for each color, first entry showsthe number of run lengths of size 2 and other entry specify the total number of runlengths of size 3 in each orientation. All these run lengths are combined to form asingle run length thereby having 8 entries for a single color. The first four entriesrepresent the total number of run length of size 2 and the other 4 entries representtotal run length of size 3 in each of the four possible orientations respectively. Thefinal run length obtained is represented with the help of a histogram having 72 × 8bins.

The method of SRLH computation is described in Fig. 3. For simplicity thequantized colors in HSV color space are denoted by alphabets a, b, c, d, e and f. Thetechnique is illustrated using 6 colors, therefore the SRLH at each orientationcontains 6 × 2 i.e. 12 bins. In real, the experiments are conducted with 72 colors andthe histogram thus produced contains 72 × 2 i.e. 144 bins. Histograms at other threeorientations are computed in a similar manner. All resulting histograms are mergedto get a single histogram as shown in Fig. 4.

The histogram are combined starting from the first color (i.e. color a). For eachcolor runs of size 2 in each of the 4 orientation are combined followed by com-bining the runs of size 3. The similar process is repeated for all the colors.

Fig. 3 The process of extraction of short run length histogram at an orientation of 0°

A Short Run Length Descriptor for Image Retrieval 5

The combined SRLH thus produced contains 6 × 8 bins (i.e. 48) for the case of 6colors. In real experiments are performed using 72 colors in HSV color spacetherefore the final SRLH have a total of 72 × 8 (i.e. 576) bins. It may be easilynoticed that the SRLH is similar to the texton histogram with higher texture details.For example in SED, each color is represented as 5 bins corresponding to 5 textonsshown in Fig. 1, However, in the present method each color is represented as 8 binscorresponding to two different sizes of run length and 4 different orientation.

Figure 5 shows images and their corresponding SRLH. It can be easily depictedfrom the figure that the SRLH for similar images are similar. This confirms theeffectiveness of SRLH in representing images. When the image is scaled, number ofpixels in the images gets changed. SRLH may be different for original and scaledimage. This problem can be solved by maintaining the proportion of pixels same inboth images. To achieve this objective normalization is applied. Let Cið0� i� 71Þ

Fig. 4 The process of combining histograms into single histogram

Fig. 5 SRLH of images a and b

6 N. Shrivastava and V. Tyagi

denotes the quantized color in HSV color space. Rn1i, R

n2i, R

n3i and Rn

4i denotes thenumber of run lengths of color i of size n at each of the four orientations respectively.The value of n can be either 2 or 3. The normalized value can be computed as:

rnji ¼Rnji

���

���

P4j¼1 Rn

ji

���

���

ð1Þ

where, rnji is the normalized bin value for orientation j. Similarly normalized binvalue for n = 3 for color i is computed. Therefore each color is represented as 8 binsin the SRLH.

4 Experimental Results

To demonstrate the performance of the proposed descriptor, experiments are per-formed on MPEG-7 Common Color dataset (CCD) [16] (dataset-1) and Corel11000 database (dataset-2) [17]. Dataset-1 (CCD) consists of 5000 images and a setof 50 Common Color Queries (CCQ) each with specified ground truth images.CCD consists of variety of still images produced from stock photo galleries, con-secutive frames of news cast, sports channel and animations. In particular experi-ments we used as ground truth, the groups of images proposed in the MIRRORimage retrieval system [16]. Chi-square distance is used as similarity measure forevaluating the performance of the system.

Figure 6 shows the retrieval performance comparison of the proposed SRLHwith other three methods MTH, MSD and SED. It can be observed that SRLH hasoutperformed others on both Dataset-1 and Dataset-2. The reason being the MSD,

Fig. 6 Average precision and recall of the different methods for a Dataset-1. b Dataset-2

A Short Run Length Descriptor for Image Retrieval 7

MTH and SED based method does not represent various discriminating patterns oftexture and hence has limited capability of describing color and texture of theimage. The proposed SRLH is flexible and can represent detail texture informationin the quantized color image. It can represent combined information gained from alltype of textons of size 2 × 2 and 3 × 3 together in a single descriptor. Also in SRLD,orientation is captured without overlap. In Fig. 6a, for top 10 images averageprecision of SED, MSD and MTH based methods are 72, 65 and 61 % respectively.At this point SRLH outperforms others with average precision of 78 %. Similarconclusions can be drawn from Fig. 6b, using Dataset-2. An example of the sampleretrieval result from our system using dataset-2 are shown in Fig. 7. Top 20retrieved images are shown for query image of horse. Top left image is the queryimage and other images are relevant images retrieved from the system.

The extraction of MTH, MSD and SED descriptors require moving textons overthe images multiple times and hence consume more time. Textons representingunique orientation of texture are moved for each color bin separately for makingtexton histogram. This takes a lot of time in high resolution images and is infeasible for real time retrieval of natural images. Proposed SRLH consumes lesstime as image is required to scan only 4 times regardless of the number of color binsused in the image. Each time run length in a particular orientation is extracted tomake histogram.

Fig. 7 Image retrieval for horses

8 N. Shrivastava and V. Tyagi

5 Conclusions

In this paper a novel short run length descriptor for content based image retrieval isproposed which can represent color, texture and orientation information of thewhole image in a compact and intuitive manner. The proposed SRLH can betterrepresent the correlation between color and texture and can describe textureinformation extracted from all type of texton in a single descriptor. In additiontexton based approaches like SED, EOAC [1], TCM and MTH consume more timein texton analysis and moving textons over the images. The proposed approach isfaster as only the run lengths from the images are to be extracted in each orientationfor the construction of feature vector. The experimental results on representativedatabases have shown that the proposed approach outperform other significantlyand hence can be used in CBIR effectively.

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